High-resolution computed tomography (HRCT) imaging of diffuse abnormalities in the lung plays an important role in the non-invasive disease management. However many interstitial lung diseases (ILDs) manifest similarly on HRCT making it difficult to reliably distinguish between them without further processing. This results in false alarms, missed idiopathic lung fibrosis (IPF) diagnoses and unnecessary invasive procedures that can have a detrimental impact on a patient's quality of life. Several approaches have been introduced to automatically detect patterns associated with multiple ILDs. Others find patterns that distinguish usual interstitial pneumonia (UIP) versus normal tissue only, IPF versus normal or emphysematous tissue only, or differentiate between subtypes of UIP. While detecting such patterns is vital to improving HRCT-based disease diagnosis, each pattern can be indicative of more than one ILD with widely varying prognosis. Thus, to be clinically meaningful there is a need to develop an automated system that can differentiate between UIP and “non-UIP” tissue regions, including normal and other interstitial lung diseases with possibly better prognosis.